1. The Field of the Invention
The present invention relates to systems and methods for automatic segmentation in speech synthesis. More particularly, the present invention relates to systems and methods for automatic segmentation in speech synthesis by combining a Hidden Markov Model (HMM) approach with spectral boundary correction.
2. The Relevant Technology
One of the goals of text-to-speech (TTS) systems is to produce high-quality speech using a large-scale speech corpus. TTS systems have many applications and, because of their ability to produce speech from text, can be easily updated to produce a different output by simply altering the textual input. Automated response systems, for example, often utilize TTS systems that can be updated in this manner and easily configured to produce the desired speech. TTS systems also play an integral role in many automatic speech recognition (ASR) systems.
The quality of a TTS system is often dependent on the speech inventory and on the accuracy with which the speech inventory is segmented and labeled. The speech or acoustic inventory usually stores speech units (phones, diphones, half-phones, etc.) and during speech synthesis, units are selected and concatenated to create the synthetic speech. In order to achieve high quality synthetic speech, the speech inventory should be accurately segmented and labeled in order to avoid noticeable errors in the synthetic speech.
Obtaining a well segmented and labeled speech inventory, however, is a difficult and time consuming task. Manually segmenting or labeling the units of a speech inventory cannot be performed in real time speeds and may require on the order of 200 times real time to properly segment a speech inventory. Accordingly, it will take approximately 400 hours to manually label 2 hours of speech. In addition, consistent segmentation and labeling of a speech inventory may be difficult to achieve if more than one person is working on a particular speech inventory. The ability to automate the process of segmenting and labeling speech would clearly be advantageous.
In the development of both ASR and TTS systems, automatic segmentation of a speech inventory plays an important role in significantly reducing reduce the human effort that would otherwise be require to build, train, and/or segment speech inventories. Automatic segmentation is particularly useful as the amount of speech to be processed becomes larger.
Many TTS systems utilize a Hidden Markov Model (HMM) approach to perform automatic segmentation in speech synthesis. One advantage of a HMM approach is that it provides a consistent and accurate phone labeling scheme. Consistency and accuracy are critical for building a speech inventory that produces intelligible and natural sounding speech. Consistent and accurate segmentation is particularly useful in a TTS system based on the principles of unit selection and concatenative speech synthesis.
Even though HMM approaches to automatic segmentation in speech syntheses have been successful, there is still room for improvement regarding the degree of automation and accuracy. As previously stated, there is a need to reduce the time and cost of building an inventory of speech units. This is particularly true as a demand for more synthetic voices, including customized voices, increases. This demand has been primarily satisfied by performing the necessary segmentation work manually, which significantly lengthens the time required to build the speech inventories.
For example, hand-labeled bootstrapping may require a month of labeling by a phonetic expert to prepare training data for speaker-dependent HMMs (SD HMMs). Although hand-labeled bootstrapping provides quite accurate phone segmentation results, the time required to hand label the speech inventory is substantial. In contrast, bootstrapping automatic segmentation procedures with speaker-independent HMMs (SI HMMs) instead of SD HMMs reduces the manual workload considerably while keeping the HMMs stable. Even when SI HMMs are used, there is still room for improving the segmentation accuracy and degree of segmentation automation.
Another concern with regard to automatic segmentation is that the accuracy of the automatic segmentation determines, to a large degree, the quality of speech that is synthesized by unit selection and concatenation. An HMM-based approach is somewhat limited in its ability to remove discontinuities at concatenation points because the Viterbi alignment used in an HMM-based approach tries to find the best HMM sequence when given a phone transcription and a sequence of HMM parameters rather than the optimal boundaries between adjacent units or phones. As a result, an HMM-based automatic segmentation system may locate a phone boundary at a different position than expected, which results in mismatches at unit concatenation points and in speech discontinuities. There is therefore a need to improve automatic segmentation.